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		<isbn>978-85-17-00088-1</isbn>
		<label>59891</label>
		<citationkey>FariaFernFranFari:2017:InFoAm</citationkey>
		<title>Influência da forma de amostragem na exatidão global e índice kappa</title>
		<format>Internet</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
		<numberoffiles>1</numberoffiles>
		<size>792 KiB</size>
		<author>Faria, Maola Monique,</author>
		<author>Fernandes Filho, Elpidio Inácio,</author>
		<author>Francelino, Márcio Rocha,</author>
		<author>Faria, Raiza Moniz,</author>
		<electronicmailaddress>maolageo@gmail.com</electronicmailaddress>
		<editor>Gherardi, Douglas Francisco Marcolino,</editor>
		<editor>Aragão, Luiz Eduardo Oliveira e Cruz de,</editor>
		<e-mailaddress>daniela.seki@inpe.br</e-mailaddress>
		<conferencename>Simpósio Brasileiro de Sensoriamento Remoto, 18 (SBSR)</conferencename>
		<conferencelocation>Santos</conferencelocation>
		<date>28-31 maio 2017</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>5976-5982</pages>
		<booktitle>Anais</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<abstract>The procedure of classifying and grouping pixels of a digital image based on its spectral characteristics using algorithms in a computational program is called image classification. The objective of this article is to evaluate the effect of sampling in the form of polygons and points in global accuracy and in the kappa index in the classification of coffee areas in the Matas de Minas region of the state of Minas Gerais. In addition, the use of cross-validation and validation was evaluated using external data in the kappa index in the classification of coffee areas in the Matas de Minas region of the state of Minas Gerais. A cut of a Landsat 8 scene was used for the area of interest. On this scene, 6,517 polygons were collected, with a mean of 12 pixels, distributed randomly throughout the study area. Based on the samples file in point format, the radiance values of each band of the Landsat 8 image were extracted. Four ways were defined in the definition of training samples of the Random Forest classifier. The procedures were performed using the software interface R and ArcGis 10.2. From the use of randomly collected points, they corroborate the accuracy, global accuracy and kappa, which are higher than those obtained by other treatments when using cross-validation, but the kappa obtained from the external validation is similar to the others.</abstract>
		<area>SRE</area>
		<type>Processamento de imagens</type>
		<language>pt</language>
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